In non-limiting examples of the present disclosure, systems, methods and devices for filtering notifications are provided. A notification may be received. A current focus state of a user may be determined. An application priority score for the application that generated the notification may be determined. A notification priority score for the notification may be determined. The notification priority score may be determined based on a contact importance score and/or a content importance score. A combined priority score may be determined from the application priority score and the notification priority score. The notification may be surfaced if the combined priority score meets a threshold surfacing value. The notification may be blocked if the combined priority score does not meet the threshold surfacing value.

Implementations set forth herein relate to an automated assistant that allows third party applications to inject dependencies to leverage automated assistant functions. Furthermore, enabling such dependency injections can allow third party applications to preserve privacy of any application content that is used during execution of automated assistant functions. In some implementations, a third party application can initialize a function with an assistant dependency using parameters that are tagged as private. Initializing a function in such as a way can allow private content communicated between the third party application and the automated assistant to be abstracted for security purposes. The abstracted content can thereafter be communicated to a remote server-such as a server hosting an extensively trained machine learning model. Intelligent output provided by the server can then be incorporated into one or more processes of the third party application without comprising security.

A vehicle control device includes a processor and a flash storage. The processor is configured to execute a plurality of processes that are associated with a corresponding vehicle control component. The processor is configured to execute an asynchronous driver that, in response to a write request of a respective process, writes vehicle status data of the respective process in a corresponding process context in the flash storage. The processor is configured to execute a synchronous driver that, in response to a write request of a predetermined process, stops the executing of the asynchronous driver on the processor and writes vehicle status data of the predetermined process in the flash storage. The synchronous driver is configured to activate the asynchronous driver again, with restoring of a saved process context, after the writing of the vehicle status data of the predetermined process in the flash storage.

Distribution and management of services in virtual environments is described herein. In one or more implementations, a service distribution and management model is implemented in which system services and applications are seamlessly distributed across multiple containers which each implement a different runtime environment. In one or more implementations, a system for distributing access to services in a host operating system of a computing device includes a host operating system configured to implement a host runtime environment, and one or more services implemented by the host operating system. The system further includes a service control manager configured to enable communication between a client stub of a service implemented in a client runtime environment and a service provider of the service that is implemented in a service runtime environment that is separate from the first client runtime environment.

A system for compressing Controller Area. Network (CAN) messages, the system comprising a processing resource configured to: obtain a CAN messages sequence including a plurality of CAN messages intercepted at a given order by at least one device adapted to monitor messages transmitted via communication channel(s) of a vehicle; group the CAN messages of the CAN messages sequence into MID groups, by a CAN MID field of the CAN messages; for each given MID group of the MID groups split the CAN messages of the MID group into field groups, wherein each field group comprises a respective field of a plurality of to fields of the CAN messages of the MID group; employ at least one compression scheme on at least one of the field groups; generate a data structure comprising the field groups; and compress the data structure using a lossless compression algorithm, giving rise to a. compressed data structure.

Systems, methods, and computer-readable media for intercepting telemetry events obtained during operation of an application and analyzing the telemetry events are provided. The telemetry events are intercepted at the library level by interposing on application calls to a native library. The telemetry events are collected and transmitted to a platform that analyzes the collected events and presents information based on the analysis.

A multi-threads tracking method, a multi-threads tracking system for an operating system and an electronic device using the same are provided. The multi-threads tracking method of the operating system includes the following steps. At least two message queue access events among two threads and one message queue are intercepted. A thread identification, a process identification, an input value and a return value of each of the message queue access events are recorded. Based on a determination of a relationship among the thread identifications, the process identifications, the input values, and the return values of the message queue access events, an In-Process dependency among the threads and the message queue is established.

A method and service to encrypt data at rest on disks that are managed by a container orchestrator (CO) using a container storage interface (CSI). The method and service including intercepting a request transferred from a CO to a CSI plugin and sending the intercepted request to an encryption proxy plugin. The method and service also including examining the request to determine if encryption is needed. In response to encryption being needed, performing encryption on the volume. The method and service also transferring the intercepted request to the container storage interface plugin.

Systems and methods are provided that intercept access to mainframe computing systems' messaging systems. For example, a method may include using a replacement messaging interface adapter to intercept a messaging request being directed from a client program to a messaging interface module of a messaging subsystem that is identified by a messaging stub interface module that implements a documented messaging interface. The method may also include performing an auxiliary function on the messaging request. The method may additionally include transmitting the messaging request to the messaging interface module of the messaging subsystem. The method may further include receiving a response from the messaging subsystem. Additionally, the method may include providing the response to the client program.

Techniques for monitoring operating statuses of an application and its dependencies are provided. A monitoring application may collect and report the operating status of the monitored application and each dependency. Through use of existing monitoring interfaces, the monitoring application can collect operating status without requiring modification of the underlying monitored application or dependencies. The monitoring application may determine a problem service that is a root cause of an unhealthy state of the monitored application. Dependency analyzer and discovery crawler techniques may automatically configure and update the monitoring application. Machine learning techniques may be used to determine patterns of performance based on system state information associated with performance events and provide health reports relative to a baseline status of the monitored application. Also provided are techniques for testing a response of the monitored application through modifications to API calls. Such tests may be used to train the machine learning model.